Postdoc in Computational Genomics for Pediatric Cancer

Dana-Farber Cancer Institute; Boston (MA); On-site; USD 72,000 - 76,000; Full time

Job summary

Dana-Farber Cancer Institute in Boston, MA, seeks a Postdoctoral Research Fellow in Computational Genomics to join the Hovestadt Lab. The role focuses on analyzing single-cell epigenomic datasets from childhood cancers to uncover tumor-specific alterations and developmental context.

The successful candidate will work with world-renowned experts, develop innovative methods, and contribute to translational goals in patient classification and treatment strategies.

Qualifications

• A PhD in Cancer Biology, Epigenetics/Genetics, Computational Biology, or related discipline.
• Experience in developing computational methods to analyze and visualize new types of genomic data.
• Understanding of human developmental biology and recent single-cell profiling efforts.
• Demonstrated ability to work collaboratively with scientists from other disciplines.
• Good oral and written communication skills.
• An outstanding academic track record.

Responsibilities

• Perform computational analyses of novel single-cell epigenomic datasets to identify tumor‑specific alterations.
• Quantify relationships between cellular origins and tumor formation using computational approaches.
• Collaborate with experts in cancer biology and epigenomics to advance translational goals.

Skills

PhD in Cancer Biology

Single-cell genomics

Computational methods

Collaborative work

Communication skills

Academic track record

Education

PhD in Cancer Biology, Epigenetics/Genetics, Computational Biology, or related discipline

Job description

Dana-Farber Cancer Institute in Boston, MA, seeks a Postdoctoral Research Fellow in Computational Genomics to join the Hovestadt Lab. The role focuses on analyzing single-cell epigenomic datasets from childhood cancers to uncover tumor-specific alterations and developmental context.

The successful candidate will work with world-renowned experts, develop innovative methods, and contribute to translational goals in patient classification and treatment strategies.